Introduction
UNDP:
The United Nations Development Programme (UNDP) is a prominent and influential organization within the United Nations system, established in 1965 with the mission of promoting sustainable development and improving the quality of life for people around the world. Operating in nearly 170 countries and territories, UNDP collaborates with governments, communities, and various partners to address a wide range of global challenges. These challenges include poverty, inequality, environmental sustainability, democratic governance, crisis response, and, importantly, climate change. UNDP has been at the forefront of global efforts to combat climate change, working tirelessly to address the urgent and long-term implications of this crisis. Through initiatives such as the promotion of renewable energy, climate resilience building, and sustainable land use, UNDP is actively contributing to a world where climate change is managed effectively, and future generations can thrive in a more sustainable environment.
Resources for the future
RFF create economy-wide and sector-specific climate solutions that are effective, efficient, equitable, beneficial to the economy, and able to achieve net-zero emissions goals. They also evaluate the physical and economic impacts of climate change, using data to assess risks, and equipping decisionmakers with the information needed to build resilience in their communities.
GIVE Model
RFF scholars have created a series of data tools that allow users to explore research data in a variety of novel ways. Greenhouse Gas Impact Value Estimator (GIVE) model, a new integrated assessment model that incorporated recent scientific advances that explicitly accounts for uncertainties in key model inputs including socioeconomic and emissions projections, the climate system, damage functions, and discount rates. This platform, referred to as the Mimi Framework, is specifically intended to provide the foundation for the scientific community’s improvement of estimates of the social cost of carbon estimates for the future.
Climate Change as a Global Challenge:
Climate change stands as one of the most pressing global challenges of our time. It is a multifaceted issue with far-reaching implications for the environment, society, and the global economy. The accumulation of greenhouse gases, primarily carbon dioxide, in the Earth’s atmosphere is leading to rising temperatures, melting ice caps, extreme weather events, sea-level rise, and disruptions to ecosystems. Climate change has far-reaching impacts on various aspects of human life and the environment, including death rates and energy consumption on a global scale.
Climate Change Impact on Death Rate:
a. Heat-Related Mortality: As global temperatures rise due to climate change, the occurrence of extreme heatwaves becomes more frequent and severe. Prolonged exposure to high temperatures can lead to heat-related illnesses and fatalities, especially among vulnerable populations like the elderly and those with preexisting health conditions.
b. Extreme Weather Events: Climate change contributes to an increase in the frequency and intensity of extreme weather events, including hurricanes, floods, and wildfires. These events can result in direct casualties and displace populations, leading to secondary health risks and potential increases in death rates.
c. Food and Water Security: Climate change can disrupt food and water supplies, leading to malnutrition and waterborne diseases. Food and water scarcity can contribute to higher mortality rates, particularly in regions with limited resources.
e. Indirect Health Effects: Climate change can exacerbate existing health problems by worsening air quality, facilitating the spread of allergens, and increasing the risk of respiratory diseases. These indirect health effects can lead to higher death rates.
Climate Change Impact on Energy Consumption:
a. Increased Cooling Demand: Rising temperatures due to climate change lead to increased demand for cooling, particularly in regions prone to heatwaves. This drives up energy consumption for air conditioning and refrigeration.
b. Reduced Energy Generation: Climate change can affect energy generation capacity, especially in regions dependent on hydropower. Droughts and reduced water availability can limit electricity production from hydroelectric dams.
c. Energy Transition: Climate change mitigation efforts often involve transitioning from fossil fuels to renewable energy sources like solar, wind, and hydropower. This transition aims to reduce greenhouse gas emissions but may initially require increased energy consumption for the construction and deployment of renewable energy infrastructure.
d. Efficiency Improvements: Climate change concerns have led to efforts to improve energy efficiency in various sectors, including transportation, buildings, and industrial processes. These initiatives can reduce energy consumption over time.
Summary:
In our project we analyzed data related to death rates and energy consumption from two distinct sources: the United Nations Development Programme (UNDP) and MimiGIVE. Our dataset spans from 2020 to 2099. Initially, we calculated average death rates and energy consumption for this century using both UNDP and MimiGIVE data to establish baseline trends.
Furthermore, we categorized countries into income levels (high, low, lower middle, upper middle) and climate groups (hot, cold, cool, moderate). Within each income group, we computed average death rates and energy consumption for each climate category, exploring potential impacts of climate on these variables.
In our analysis, we not only calculated the average death rates and energy consumption but also meticulously determined the differences between the UNDP data and MimiGIVE for average death rates and energy consumption across all income groups. Additionally, we extended this comparative analysis within each income group, considering all four climate categories (hot, cold, cool, and moderate). By examining these differences within each income and climate group, our aim was to provide a comprehensive assessment of how the UNDP dataset compares to MimiGIVE under diverse economic and climatic conditions. These distinctions offer valuable insights into the reliability and accuracy of both the UNDP dataset and MimiGIVE in the context of income and climate variations.
Through our examination of death rates and energy consumption across different economic and climatic conditions, our research illuminates the intricate interplay between income, climate, and key demographic and energy consumption indicators. This holistic approach enriches our understanding of these dynamics and highlights potential variations in the datasets.
Methodology:
Data Sources:
Acquiring MimiGIVE Data: The MimiGIVE data was acquired using the Julia programming language. This data source provides crucial information for our analysis, including death rates and energy consumption.
Climate Categorization Data: The World Bank’s climate data was utilized to categorize countries into climate groups (hot, cold, cool, and moderate). Additionally, country shapefiles from the Natural Earth website were employed for mapping purposes.
| Countries | Avg Temp GIVE | Avg Temp UNDP |
| Hot | 80.67 | 81.18 |
| Cold | 44.55 | 45.19 |
| moderate | 75.51 | 75.13 |
| cool | 64.47 | 64.16 |
Table:1 Countries categorized as hot, cold, moderate, and cool, with corresponding average temperatures reported by GIVE and UNDP datasets.
Mapping with ArcGIS Pro: ArcGIS Pro, a robust geospatial analysis tool, was employed for creating maps. The geographic data, including country boundaries, was used in conjunction with ArcGIS Pro to visualize the findings.
Spatial Analyst Tools: Within ArcGIS Pro, tools such as the Join Tool and Spatial Analyst tools were utilized for mapping purposes. These tools were instrumental in merging data and conducting spatial analyses to create informative maps.
Energy Consumption Calculation:
Calculating Energy Consumption from GIVE Data: To determine energy consumption from the MimiGIVE dataset, the following formula was applied:
energy_costs_dollar * 1e9 / 13.8012.
This calculation allowed us to express energy consumption in gigajoules (GJ).
Normalization with Population Data: The calculated energy consumption was further normalized by dividing it by the average population of the respective countries. This normalization ensured that energy consumption values were contextually relevant and comparable across countries with varying population sizes.
By employing these methodologies, we conducted a comprehensive analysis of death rates and energy consumption, considering income levels and climate categories. The utilization of Julia programming for data acquisition and ArcGIS Pro for mapping, along with the normalization of energy consumption, allowed for a holistic exploration of the interplay between income, climate, and key indicators, enhancing our understanding of these complex dynamics.
Results:
Global Deathrate Maps (MimiGIVE)
We generated a comprehensive global map using ArcGIS Pro to visualize death rate data from MimiGIVE from the years 2020 to 2039. The map employs a color ramp to represent death rates across various countries, offering a clear depiction of regional disparities in mortality trends during this period.
Upon examination of the map, several noteworthy observations emerged. Notably, certain regions exhibited relatively higher death rates when compared to others. These regions included Russia, Iran, Algeria, Libya, Egypt, and Saudi Arabia.
In our continued exploration of death rate trends, we generated an additional map using ArcGIS Pro, focusing on the time frame from 2040 to 2059. This map illustrates alterations in death rates over this specific period.
Notably, there was a positive increase in death rates in these countries. Among these regions, Saudi Arabia stood out, exhibiting a notably higher death rate during this timeframe. This observed increase in mortality rates in Saudi Arabia suggests a critical shift the climate change.
| Figure 1: Global Death-rate Map for the years 2020-2039, illustrating projections with MimiGIVE model |
| Global Death-rate Map 2020-2039 (MimiGIVE) |
| Figure 2: Global Death-rate Map for the years 2040-2059, illustrating projections with MimiGIVE model |
| Global Death-rate Map 2040-2059 (MimiGIVE) |
| Figure 3: Global Death-rate Map for the years 2040-2059, illustrating projections with MimiGIVE model |
| Global Death-rate Map 2080-2099 (MimiGIVE) |
Our research extended into the years 2080 to 2099, and the subsequent map provides a striking representation of evolving death rate trends during this timeframe. What stands out prominently is the substantial increase in death rates observed globally.
It is essential to emphasize that the observed increases in mortality cannot be isolated from the broader context of extreme climate changes. As climate change accelerates, it exerts profound and far-reaching effects on ecosystems, health, and socio-economic systems worldwide.
The relationship between rising death rates and extreme climate changes is increasingly apparent. Elevated temperatures, more frequent and severe weather events, and shifts in disease vectors all contribute to health challenges that are compounded by climate-related factors. While our analysis highlights global trends, it is vital to recognize that specific regions may experience these impacts differently, often magnifying vulnerabilities in areas with limited resources and adaptive capacity.
GIVE vs. UNDP Energy Consumption Trends
We conducted an in-depth analysis of energy consumption trends, utilizing data from both the GIVE and the United Nations Development Programme (UNDP). Our examination focused on the average energy consumption from 2020 to 2099 for each dataset.
When contrasting the energy consumption graphs between GIVE and UNDP, distinct trends emerged. In the GIVE dataset, energy consumption predominantly exhibited positive values across the majority of countries. Conversely, the UNDP dataset presented a contrasting picture, with energy consumption primarily displaying negative values for a substantial number of countries. Svalbard and Jan Mayen has the lowest energy consumption of -5.180GJ according to the UNDP data. While Zambia has the positive energy consumption of 0.1255GJ. While in GIVE energy graph the lowest energy consumption is for the Canada with -3.325GJ. while the highest is for kwait 64.469GJ.
| Figure 4: Global Energy Consumption Trends – GIVE Data |
| Figure 5: Global Energy Consumption Trends – UNDP Data |
Disagreement between the two energy estimates by income group:
In our comparative analysis of energy consumption between the UNDP and GIVE datasets, we observed significant disparities across income levels. Starting with the ‘Rich’ income group, UNDP recorded a negative energy consumption value of -1.6415 GJ/person, while GIVE reported a higher positive value of 33.47 GJ/person. Similarly, in the ‘Upper Middle Income’ group, UNDP reported -0.9764 GJ/person, contrasting with GIVE’s 79.94 GJ/person. For the ‘Lower Middle Income’ group, UNDP’s value was -0.4767 GJ/person, while GIVE’s value was 66.55 GJ/person. In the ‘Poor’ income group, UNDP reported negative values of -0.1923 GJ/person/person, whereas GIVE indicated significantly higher positive energy consumption at 25.55 GJ/person.
GIVE consistently reports significantly higher energy consumption values compared to UNDP.
Figure 6: Histogram of Energy Consumption Discrepancies between GIVE and UNDP Datasets Across Income Groups
| Countries | UNDP(GJ/person) | GIVE(GJ/Person) | Difference(GJ/Person) |
| Poor | -0.1923 | 25.55 | 25.7423 |
| Rich | -1.6415 | 33.47 | 35.1115 |
| Lower Middle Income | -0.4767 | 66.55 | 67.0267 |
| Upper Middle Income | -0.9764 | 79.94 | 80.9164 |
Table 2:UNDP and GIVE Energy Consumption per Person with Differences Across Income Categories.
To delve deeper into these disparities, we examined the differences in energy consumption between UNDP and GIVE across income levels. Our analysis revealed that the energy consumption difference was most pronounced in upper-middle-income countries, where deviations between the two datasets were higher, i.e., 80.9164 GJ/person. This indicates that the GIVE dataset reports significantly higher energy consumption for this group compared to UNDP. For high-income countries, the difference in average energy consumption between both datasets is 35.1115 GJ/person, while lower-middle-income countries exhibit a relatively smaller energy consumption difference of 67.026 GJ/person. In contrast, lower-income countries showed a difference in average energy consumption of 25.7423 GJ/person. These values also indicate that the GIVE dataset reports significantly higher energy consumption compared to UNDP.
Disagreement between the two Energy consumption estimates by climate group
The table below shows the differences in energy consumption between the GIVE and UNDP datasets across different income groups and climate categories. The values represent GIVE minus UNDP energy consumption:
Poor Income Group: In the “Cool” climate category, GIVE reports higher energy consumption compared to UNDP by approximately 4.0694 GJ/person. In the “Moderate” climate, this difference increases to approximately 4.4019 GJ/person in favor of GIVE. In the “Hot” climate, GIVE’s energy consumption is higher than UNDP by approximately 3.50836 GJ/person
Lower Middle Income Group: In the ‘Lower Middle Income’ category, the differences in energy consumption are also notable. In the “Cold” climate, GIVE reports higher energy consumption compared to UNDP by approximately 4.665 GJ/person. In the “Cool” climate, the difference is even more significant, with GIVE showing higher energy consumption by approximately 7.3844 GJ/person. In the “Moderate” climate, GIVE’s energy consumption exceeds UNDP by approximately 5.978 GJ/person, and in the “Hot” climate, it’s higher by approximately 5.162 GJ/person.
Upper Middle Income Group: Within the ‘Upper Middle Income’ category, the disparities in energy consumption are pronounced. In the “Cold” climate, GIVE reports higher energy consumption compared to UNDP by approximately 6.14 GJ/person. In the “Cool” climate, the difference is higher, with GIVE showing higher energy consumption by approximately 8.088 GJ/person. In the “Moderate” climate, GIVE’s energy consumption exceeds UNDP by approximately 10.46 GJ/person, and in the “Hot” climate, it’s higher by approximately 8.362 GJ/person.
Rich Income Group: Finally, in the ‘Rich’ income group, differences in energy consumption between GIVE and UNDP datasets are significant. In the “Cold” climate, GIVE reports higher energy consumption compared to UNDP by approximately 3.933 GJ/person. In the “Cool” climate, the difference is substantial, with GIVE showing higher energy consumption by approximately 5.3595 GJ/person. In the “Moderate” climate, the difference is even more pronounced, with GIVE’s energy consumption exceeding UNDP by approximately 19.791 GJ/person. In the “Hot” climate, the difference is the largest, with GIVE’s energy consumption higher by approximately 31.55 GJ/person compared to UNDP.
| Countries | Cold(GJ/person) | cool(GJ/person) | moderate(GJ/person) | Hot(GJ/person) |
| Poor | 4.0694 | 4.4019 | 3.50836 | |
| Lower Middile Income | 4.665 | 7.3844 | 5.978 | 5.162 |
| Upper Middle Income | 6.14 | 8.088 | 10.46 | 8.362 |
| Rich | 3.933 | 5.3595 | 19.791 | 31.55 |
Disagreement between the two deathrate estimates by income group
The ‘Rich’ income group exhibits an average death rate of 0.1119, equivalent to 1.69% of the total. In contrast, the UNDP dataset shows an average death rate of 0.1250, constituting 1.25% of the total. The discrepancy between the two datasets for this income group is -0.1135, with a percentage difference of 1.25%.
For the ‘Upper Middle Income’ group, the average death rate is 0.0862, representing 2.06% of the total. In contrast, the UNDP dataset reports an average death rate of 0.1887, constituting 1.89% of the total. The difference between the two datasets for this income group is -0.0664, with a percentage difference of 1.89%.
For the ‘Lower Middle Income’ group, the average death rate is 0.0956, accounting for 1.92% of the total. In the UNDP dataset, the average death rate is 0.1852, making up 1.85% of the total. The difference between the two datasets for this income group is -0.0642, with a percentage difference of 1.85%.
The ‘Poor’ income group has an average death rate of 0.0646, representing 4.16% of the total. In comparison, the UNDP dataset reports an average death rate of 0.1121, which accounts for 3.85% of the total. The difference between the two datasets for this income group is -0.0475, with a percentage difference of 3.85%.
| AverageDeathrate 2020_2099 | Percentage % | UNDP_Average Death_rate(2020_2099) | Percentage% | Difference | |
| Poor | 0.0646 | 4.161566707 | -0.047502 | 3.846315789 | 0.112102 |
| Rich | 0.1119 | 1.694889582 | -0.113535 | 1.25 | 0.225435 |
| lower Middle Income | 0.0956 | 1.92160804 | -0.064198 | 1.851847578 | 0.159798 |
| Upper Middle Income | 0.0862 | 2.060190054 | -0.066374 | 1.886804253 | 0.152574 |
Table 4: Table showing Deathrate Discrepancies between GIVE and UNDP Datasets Across Income Groups
Figure 7: Histogram of Deathrate Discrepancies between GIVE and UNDP Datasets Across Income Groups
Disagreement between the two deathrate estimates by climate group:
Cold Climate Group: In the Cold climate group, death rate estimates vary across income levels. In the rich income category, there is a significant increase to 0.88085. For the upper middle-income category, the death rate is slightly lower at 0.08943944, and in the lower middle-income category, it is 0.09232.
Cool Climate Group: In the Cool climate group, death rate estimates consistently increase across income levels. The rich category has the highest death rate estimate at 0.119910836. The upper middle-income category sees a similar trend with a death rate of 0.115452685, while the lower middle-income category is slightly lower at 0.11279. The poor category has a death rate of 0.115367436.
Moderate Climate Group: Within the Moderate climate group, death rate estimates demonstrate variability across income levels. The rich category sees a notable rise in the death rate estimate, reaching 0.09235. The upper middle-income category remains relatively stable at 0.07918. The lower middle-income category shows an increase to 0.082489262, and the poor category has a death rate of 0.0765174.
Hot Climate Group: For the Hot climate group, death rate estimates also exhibit variation across income levels. The rich category has the highest death rate estimate among the groups at 0.08977. The upper middle-income category sees a modest increase to 0.08724, while the lower middle-income category is slightly lower at 0.08364. The poor category has a death rate of 0.085185559.
| Countries | Poor | lower Middle Income | Upper Middle Income | Rich |
| Cold | 0.09232 | 0.08943944 | 0.88085 | |
| cool | 0.115367436 | 0.11279 | 0.115452685 | 0.119910836 |
| moderate | 0.0765174 | 0.082489262 | 0.07918 | 0.09235 |
| Hot | 0.085185559 | 0.08364 | 0.08724 | 0.08977 |
Table 4: A Comparative Analysis of Death Rates for income and climate groups.
Conclusion:
Our project aimed to analyze data on death rates and energy consumption from 2020 to 2099, using UNDP and MimiGIVE datasets. We explored the impact of climate on death rates and energy consumption within income and climate groups and compared the UNDP and MimiGIVE data. Additionally, we aimed to identify areas where further research is needed, shedding light on gaps in our understanding of global development and sustainability dynamics. Identified areas of disagreement to highlight areas for future research.
